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October, 2020

How IoT is Improving the Quality of Healthcare

How-IoT-is-Improving-the-Quality-of-Healthcare

IoT is redefining Healthcare. Look around and you will find people with smart devices that track their every move, calculate their intake, and give them trends on this data.

Primitively caregivers and hospitals were using telemetry to remotely gather data for improving patient care. The primary aim of preventive healthcare was to deliver personalized care, improve patient care without spending a huge amount of money.

The Internet of Medical Things (IoMT) is driving the future of the healthcare industry. It can bring better outcomes; improve efficiency and make healthcare more affordable, as caretakers are increasingly resorting to more self-care due to increased awareness. To achieve this, healthcare providers must make use of the latest technology in a more systematic way.

The Scope of IoT is Getting Bigger and Better in:

  • Preventive healthcare: by use of wearables.
  • Patient tracking: in monitoring patient movement and health analysis.
  • Geriatric care: in tracking senior citizens which is a large market for IoT and medical devices.
  • Real-time location tracking: in tracking medical devices, people, and asset movement.

It is predicted that the revenue from smart wearables will increase to around $22.9 billion by the end of 2020. Experts from P&S Market research expect that the Internet of Things industry will grow at a compound annual growth rate (CAGR) of 37.6% between 2015 and 2020.

Wearables for Preventive Health Analysis 

Imagine a wearable is used for preventive health analysis. The term wearable in health parlance should not be restricted to just fitness tracking devices worn in the wrist that is used to monitor personal health.

The term wearables should go beyond the tracking of physical activities. It could be used as a communication device or it could even be a device that interacts with other devices like an Apple watch. It could be a device in the body, on the body or near the body like a medical app that helps track personal health;

Some of the leading medical apps that are already disrupting the healthcare market are:

  • Philips’ Medication Dispensing Service
  • Boiron Medicine Finder App
  • Future Path Medical’s Urosens

Digital Hospitals Making a Headway

The Healthcare industry is increasingly leveraging modern technology and digital hospitals are making headway such as the Humber River Hospital in Toronto Canada and the Medical Center at Mission Bay San Francisco. Innovative approaches towards engaging robots in the radiology and other departments are also disrupting the way healthcare is delivered.

Deakin University Australia, in partnership with Telstra Australia, has developed haptics-enabled robots that can perform ultrasound diagnostics remotely. This means the patient need not be in the same place as the sonographer conducting the ultrasound.

IoMT for Improved Healthcare

There are over 97,000 mobile healthcare apps as of 2019 and the mHealth app marketplace is expected to grow 15 times faster, according to a survey. Another survey indicates that users prefer digital services to communicate with doctors, monitor health, and collaborate with caregivers with ease.

Final Thoughts

IoT is clearly here to stay. With the cost of Hardware coming down, there’s no dearth in demand for wearables as this space is just short of an explosion, especially, in the mature markets.

IoT devices and apps are helping healthcare professionals in providing better care for their patients. There is definitely much scope for advancement for IoT in the healthcare sector.

Looking to leverage IoT technology for a healthcare solution? Kindly Contact Us here.

Improve Retail Business With Machine Learning

Alex Thompson Data and AI October 22, 2020
Improve-Retail-Business-With-Machine-Learning

Technology has transformed how customers and brands communicate with each other. Shoppers were once dependent on face-to-face, in-store interactions to make purchases and receive support. Now, shoppers do their research before entering a store (81 percent of shoppers conduct online research before buying) and hardly rely on salespersons to help them make decisions. Retailers, however, have understood that by embracing technology, they can extend their storefronts to their customers’ fingertips.

Shoppers can make purchases from within social media apps and compare prices without leaving a store. While these technologies have propelled the retail industry further into the digital age, the technology that is still evolving will have the largest impact on the future of the customer service and retail industries.

Embracing Big Data

More retailers are tracking customer shopping habits through data sources such as social media, purchase history, consumer demand, and market trends. By relying on big data technology to gain a deep understanding of shoppers and their buying trends, retailers can maximize customers’ spending and encourage customer loyalty.

According to research by Accenture report, 70 percent said that big data is necessary to maintain competitiveness, and 82 percent agreed that big data is changing how they interact with and relate to customers.

Matching Products with People

Machine learning technology boosts the reach of big data analytics and can help create an exceptional shopping experience. Innovative retailers can tap into the power of machine learning algorithms to do things like determine available products from outside vendors or recommend the quantity, price, shelf placement, and marketing channel that would reach the right customer in a particular area.

Further, the capability to automate everything through advanced analytics and machine learning soon will mean that basic customer service will be performed by bots that can predict our needs and provide service in the fastest, most immediate way possible: by offering us items we didn’t know we needed. As retailers gain more insight into their customers and products, machine learning will be able to match buyers and sellers based on buyers’ needs and product availability.

Digital Assistants

Shopping is becoming increasingly programmatic. In the future, services like digital assistants (Siri, Cortana, etc.,) will learn more about us and offer us relevant and personalized product offers. Say, for example, you use a particular brand of perfume. Your digital assistant will learn your shopping and usage habits and offer you the best deal on the product at the right time. It might even place the order for you.

Improving the backend

Machine learning and advanced analytics will not only change how we shop and provide customer service, but also simplify how retailers perform basic operations. Data science and machine learning give us the ability to automate so much of the heavy lifting required to find insight within a pile of data. With these tools, retailers can find useable and useful data to change the shopping experience for consumers.

Technology enables us to create an index of every product in the world, enabling retailers to offer customers the best prices, keep products adequately stocked, and track competitors’ minimum-advertised-price violations. A central database of the world’s product information enables retailers to offer the best shopping experience for buyers.

An innovative-technology approach to customer service and commerce will combine data about our behaviors and choices with data about products and product attributes to create the best shopping experience. This approach takes the guesswork out of purchasing and makes the shopping experience more cherishable for everyone.

Top 5 Big Data Trends In 2020

Alex Thompson Data and AI October 21, 2020
Big Data Trends

When the world big data rapidly expanded a decade ago, there were no signs that they would slow down. It is primarily aggregated across the internet, such as social networking, web search requests, text, and media files. IoT devices and sensors produce another gigantic share of data. These are the main reasons for the global big data market growth of 49 billion dollars.

Spark will Widespread

Apache Spark is a platform for data processing that can easily perform tasks on very large data sets and also spread the functions of data processing over many devices, either on its own or in combination with other distributed computing resources. These two qualities are important to the worlds of big data and machine learning that require vast data stores to sharpen the masses of computer power. Spark removes some of the programming burdens from developers with an easy-to-use API which sums up many of the grunt tasks of distributed computing and big data processing.

Apache Spark has been one of the main computing frameworks that spread throughout the world. Spark offers native binding for Java, Scala, Python, and R languages, and supports SQLs, data sharing, machine learning, and graphic processing. The Spark software can be used in several ways.

The convergence of IoT, Cloud, and Big Data

In order to facilitate interaction between machines and humans (M2H) and machines (M2 M), the Internet of Things is an opportunity for simplifying operations in many areas. Until now it has been greatly improved. In most cases, sensor-generated data is transmitted for analysis to the Big Data System and final reports are made. This is also the main interconnecting point of the two technologies.

For the next ten years, IoT is expecting a future of $19 trillion in the web industry, which will give room for more IoT and Big Data research and development.

Cloud computing plays a significant part in the storage and management of the data by generating an immense amount of data. It is not only about big data growth but also the development of platforms such as Hadoop for data analytics. As a consequence, it provides new cloud computing opportunities. Therefore, service providers like AWS, Google, and Microsoft have cost-effectively their own Big Data Solutions for businesses of all sizes.

Mixed Reality will improve Data Visualization

AR and VR have gained a lot of traction among customers in the past few years. With the launch of Pokémon Go, Augmented Reality had garnered around 100 million users within just a few weeks of launch. Though AR or VR might not be very useful for large corporations, the concept of Mixed Reality might very well be. Mixed reality combines the virtual world with our real-world and devices like Microsoft Hololens are already gaining traction. Mixed Reality will offer huge opportunities for organizations to better perform tasks and also to better understand the big data.

Deep Learning

Deep learning is an advanced form of machine learning which is based on neural networking. Deep learning help recognize specific items of interest from massive volumes of unstructured data. It is mostly useful for learning from huge volumes of structured and unstructured data. Thus businesses and organizations should pay more attention to deep learning algorithms to deal with the heavy influx of big data.

Data Virtualization

Data virtualization will see strong momentum this year. Data virtualization has the ability to unlock the hidden concepts and conclusions from a large set of data. It also allows enterprises and organizations to retrieve and manipulate data on the go.

To address big data problems, the management and use of computer and data-intensive systems require huge amounts of highly distributed datagrams. Virtualization offers the additional flexibility needed to realize large data platforms. Although virtualization is theoretically no prerequisite for big data analysis, in a virtualized environment software frameworks are more effective.

Conclusion 

As mentioned earlier, this year will be an exciting year for big data, and analytics systems will become the top priority for organizations. These systems are expected to perform well operationally, and fulfill promises of business value to the organization.

Future of Virtual Assistants: Is it the rise of machines already?

Virtual-Digital-Assistants

The term Rise of Machines should bring you memories of the movie Terminator series and Skynet.

Virtual digital assistants (VDAs) are rapidly gaining traction in both consumer and enterprise markets. So, what are these VDAs? Virtual digital assistants are nothing but automated software programs or platforms that help the user through understanding natural language in written or spoken form. Going by the virtual assistant demand, the device is poised to digitally transform the user experience.

Apart from smartphone-based virtual digital assistants, which are widely popular, VDAs are also beginning to enter the ecosystem of smart home assistants and other device types like fitness trackers, PCs, and automobiles. This rapid proliferation of virtual digital assistants is due to the accelerated innovation and scalability of associated technologies like AI and NLP (natural language processing).

In the future, you will be able to chat with your car about the best locations to visit. Your car will display the best possible route after analyzing driving time and getting your preferences conversationally. All these advancements will be due to the tremendous power and rise of digital assistants.

From the consumer-oriented virtual assistants like Siri, Amazon Echo, etc., to dedicated software for business use cases, future virtual personal assistants are going to digitally transform the customer experience. Thus, an enterprise must build a VDA to stand out from the crowd. Here are some of the steps to build an effective virtual assistant.

Step 1: Build a flawless speech-recognition system. This process requires acoustic modeling, voice modeling, and a speech recognition engine

Step 2: Enable Natural Language Processing (NLP) which is the basic intelligence required to process semantics of a user’s speech input.

Step 3: Integrate machine learning or AI to improve the intelligence of the virtual digital assistant. This allows VDAs to learn, understand, and adapt based on the information available.

Step 4: Since responses should be instantaneous, VDAs need large scale systems that provide the power required for processing large amounts of data.

Step 5: Finally, all these modules should be secured using an API gateway to interface with several other systems. It is worth mentioning that, VDAs should be designed for a mobile-first and cloud-based environment.

HOW WILL THE FUTURE OF VIRTUAL ASSISTANTS IMPACT CUSTOMERS AND BUSINESSES?

VDAs would soon lead to the era of high customer satisfaction. With VDAs, there is tremendous opportunity to better engage customers and employees alike.

The consumer side of virtual assistants will become more proactive. Virtual assistants will learn more about you from your texts, searches, emails and it will start suggesting or predicting what you need, even before you ask.

Nexus – Inspiring Innovation

Alex Thompson Product October 21, 2020
Nexus-–-Inspiring-Innovation

Culture and Employee Engagement  

Every business strives to get this right – it’s work culture. Workplaces are usually very dynamic and varied in its surroundings. Developing an inclusive work culture involves the strengths and weaknesses of employees.  

Employee engagement is also one of the most contentious activities companies routinely endureAlthough there is little controversy about the importance of employee participation, the quality of the process, and how companies use it to create a high-performance workforce are very relevant. 

Life at TVS Next 

TVS Next helps clients reimagine, design, and develop software to make this world a better place. We work with people and organizations that are driven by ambitious goals.  

Career Aspiration
But building a better world starts with becoming better individuals, teams, and communities. We therefore consistently strive to help every Nexian evolve and achieve an inspired career path. Help them unleash their unlimited human potential!  

Growth of Nexians  

The career path is one of the most important journeys in our life. Leading oneself through career zigzags is like walking through a hedge maze, and what helps navigate the uncertain terrain is when aspirations are not capped by a ceiling, but at the same time grounded 

Career Growth
To achieve one’s potential, expectations are vital and must be guided to be meaningful. We look forward to a career path that is meaningful and leads us to make a positive impact around us and the world at large. Not a job, but a path that supports our aspirations. However, a career is more than just a job, or work, or occupation. It also includes one’s progress through life, growth, and development in vocational and avocational areas of life.

From setting high aspirations to realizing them we require a growth path. A path that is holistic, performance-driven, and enriches the value delivered. To ensure success, a growth path cannot be tread alone but in teams and requires collaboration, leadership, and a resilient attitude.    

Career growth paths require to go beyond individual contributions and take a progressive approach.A career path gives the employee a sense of direction, a way to assess career progress, and an opportunity to achieve career goals and milestones along the way. 

Career Transformation
 

But to sustain a growth path, it is vital to look beyond team contribution and solve complex problems that can contribute to the greater good of the organization and society.

Nexus – Inspiring Innovation 

Nexus is a People Experience Platform built by Nexians to help us achieve career and culture transformation from being performance-driven to a leading innovative culture.

Nexus is built on three pillars that enable transformation and measure growth Performance, Success, and Innovation.

This is just the beginning! 

Customer Cognizance Through Omnichannel

Decor Cover

In the last decade, marketers have progressed slowly but steadily. It was not uncommon for marketers, 10 years ago, to focus primarily on their domain and use SEO and SEM for traffic management. Marketers cannot condone such a gap in their perception of the customer in today’s omnichannel environment. 

Omnichannel

Recently, marketers have also begun to use other channels such as mobile displays, social media, and programmatic displays. Initially, these channels have been managed independently to produce “multi-channel marketing,” but later, they integrated their channels to provide a “cross-channel” brand message. 

For companies that are focused on attracting and retaining consumers with more choice and higher expectations, omnichannel marketing has become a priority.  

Like other trades in the modern era, omnichannel is an area of tremendous potential, frequently debated, but seldom well accomplished. This practice includes addressing obstacles and uncertainties that hinder other businesses from making their true vision happen. 

There is no doubt that data is growing at an exceptional rate and, especially, the increasing number of consumer data fundamentally alters the way brands work in the region.  

Given the impending future without cookies, improvement in personalized targeting increased with consented customer information such as Consumer Data Platforms and Consumer Panels. The growth in 1st-party data provides marketers with an excellent view of the customers in their online world. Companies are also increasingly equipped to furnish marketers with offline customer data, such as Point-of-Sale (POS) systems, geofencing, and beacons. 

Impacts of Isolated Online and Offline Data 

The inability of a brand to integrate its data online & offline adversely affects the ability of a company to intelligently trigger the omnichannel, holistic strategic considerations, and exploit media usage nuances. Their value at a scale is all at the ultimate cost of the marketer’s desired return on media expenditure due to a lack of understanding and a lack of attribution between online & offline world. Silo-based media and two-dimensional digital media consumption are still high across the Asian Pacific both for online and selected offline outlets. 

Digital’s share of ad spend across the region will expand from just over 50% in 2019 to 59% by 2023, with online video contributing 20%, and although less spent on conventional offline media platforms in general, these traditional channels still account for a substantial proportion of promotional budgets. Out-of-Home, is still going strong in the South East Asia region, having grown +19% in 2019. In planning for these campaigns, brands are of course using the rich data for both their online and offline consumers to inform their segmentation and targeting. The issue is that both data sets live in isolation from one another, and media planning teams are unable to use online data to influence offline media planning and measurement, and vice versa. The lack of a bridge results in media planning that is divorced from the complex reality of how consumers behave across channels.  

Consumers aren’t two-dimensional, unlike our existing online and offline data usage. They do not live in online and offline siloes, and their purchases are no longer predictable.  

Most marketers are familiar with the all-round concepts of web-building (where consumers collect information about the goods online and then buy them offline) and showrooming (where consumers are visiting the stores physically to look and feel the product before making their purchase online), But even those concepts are oversimplified representations of how dynamic the purchase journey has become. Consumers do not flow constantly through a funnel but a maze of actions and connections with the brand in the path to purchase as they move through online and offline touchpoints. In fact, understanding this maze and getting a single view of the consumer when they seamlessly move online and offline is critical. 

Bridging the Gap Between Online and Offline Data 

Digital networks have been leading the way since they attempted to link or bridge the gap between offline and online platforms. Why is it required? Why cannot customers get a different experience for each channel? It is because, with the advent of digital platforms, consumer perceptions have changed drastically. 

omnichannel

Easier to communicate with brands using digital technologies led them to expect smoother and seamless offline/physical brand interactions. In short, they wanted an all-round experience that would blur the distance between offline networks.

Cross-channel marketing is all about delivering marketing strategies to consumers through different platforms both offline and online. 

The marketers must merge networks with each other in order to bridge the gap between the offline-online, to transfer data, and to process it from one channel to the other. Different technologies like APIs, RFID, etc. may be used. 

Vanity URLs often act as a cross-channel marketing tool that reduces offline distance. Such URLs are shortened web addresses that are easy for customers to recall. Using these URLs on offline assets like a flyer, print ad, or banner, consumers can be motivated to search the URLs on phones, tablets, or computers. 

Marketers will sell the goods online and have their customers picked up from the brick and mortar shop to create a seamless shopping experience.  

And salespeople can also allow products bought online to return to a physical shop. Items bought from physical stores can also be returned digitally by requesting a pick-up of the items. The cashback can also be credited directly to the customer’s bank account. 

There was one of America’s famous retailers who experimented in bridging the gap between online and offline data—Macy’s. The company noticed that consumers frequently review items on their website before visiting a physical store. The company thus wanted to give consumers exposure in the store in order to see if their favorite items were available in the nearest shop. Macy also offered various shipping options, such as home delivery, click-and-collect, etc., which really resonated with consumers and helped increase the company’s revenues. 

Marketers may use location-based targeting to drive consumers to purchase products while they are on the go. 

It can be achieved by providing a smartphone push notification when they enter or exit a geofenced area. They can also be monitored by beacons when entering the shop. Marketers use beacon technology to give customers a personalized experience while they shop. Therefore, the offline-online link is enhanced and at each stage of the customer journey, there is an interaction. 

For instance, a person enters the mall and triggers the geofence. An app pushes notification pops-up on his device about some exciting offer going on in his favorite store. The customers are encouraged to visit the store even though they do not intend to go to it in the first instance in real-time. 

Conclusion 

When consumers expect enriching experiences across all touchpoints, irrespective of online and offline platforms, marketers will strive to achieve that. A clear transition should be made from an offline to an online platform from the consumer viewpoint, and vice versa. To step up the ladder, marketers must follow omnichannel marketing strategies that primarily bridge the gap from offline online to provide consumers with a clear and seamless experience across all available channels. 

Will Automation Eliminate Manual Testing?

Will-Automation-Eliminate-Manual-Testing

We live in an era where software development has been revolutionized by AI (Artificial Intelligence) & ML (Machine Learning). It is expected that manual testing will be taken over by automation with its new developments and advancements, but that is not the case. Software manual testing has been around for many decades since initial software development, and the industry has taken multiple shifts. However, its scope remains the same. In this article, let us explore the impact automation testing has on manual software testing.

Why is manual testing still relevant?

New Projects: Projects in pilot phases that begin as a concept and take shape during early sprints require manual testing. Using automation testing during the initial phase of software development would be expensive as it undergoes continuous changes. But, leveraging direct human involvement in testing through manual testing would be cost-efficient and easy to accommodate changes.

End 2 End Testing: Automated testing can be used to test single systems or integration levels in detail. Whereas End 2 End testing involves multiple systems and requires manual testing. Automation testing that runs an End 2 End test scenario has many challenges, especially systems that have different tech stacks. Design changes involving systems in End 2 End testing impacts maintenance cost.

Maintenance Cost: For small projects or components, automated testing costs are higher than manual testing. Performing quick manual testing would suffice for smaller projects/ components that undergo frequent changes rather than updating test scripts after rerunning those tests manually.

UX Testing: Maintenance costs are proportional to UX changes. With each UI/UX change, test cases break and raise a false fail. When changes are encountered in a script, there is rework / maintenance to achieve the test pass. This impacts the next UI changes again. So, for an application with frequent UI/UX changes, automation testing is costlier than manual software testing.

Visual Testing:

While there are few automation tools available in the market, AI & ML are incorporated with visual testing to achieve 100% test resulst. But the number of hours required to train AI to understand minute changes in UI would be expensive than performing manual tests. Sometimes, human eyes can find a little misaligned text box, which could be challenging for an automation tool. Such automation tools with AI & ML are expensive compared to the advantages of manual testing.

User Acceptance Testing: There is no way usability testing could be automated. Beta users/client teams must experience the end product by simulating user experience using manual testing.

How automation testing can be leveraged?

Let us discuss the areas where automation has to be implemented to support manual testing benefits.

Regression: When a part of the product is regression and the product or UI changes, tests have to be automated using open source software. Using automation testing can therefore save manual testing time.

Integration Testing: API level automation can be quickly created like manual testing. Manual software testing tools like Postman enables us to create tests that can be automated using the runner feature. When manual testing is performed, requests are stored as a collection. This stored collection can be run any time as a test suite to rerun the test scenarios.

Smoke Test on CI/CD: Automating test scripts for smaller projects are expensive. However, using smoke test scenario would reduce the cost. Smoke tests undergo changes to get added to CI/CD pipeline for project code deployment capturing blocker/showstopper issues during code deploy to QA/Stg environment, before the code is released to production.

Conclusion:

Manual and automation testing complement each other. Manual software testing services are as important as automated testing, and there can be no project that is purely manual. There will always be an area where automation can be leveraged with open source tools that are no-cost and low maintenance. No project can completely use automation testing as client expectation keeps changing; manual testing is the way to handle frequent changes and ad-hoc testing requests. It is up to the project management to decide how and where automated and manual testing have to be implemented to provide a customer satisfied product delivery.

Emerging Healthcare Trends Post Covid-19

Emerging-Healthcare-Trends-Post-Covid-19

COVID-19 reveals how fragile many world’s health systems and services are, forcing countries to make difficult choices to best meet people’s needs. Though patient care systems have progressed long before coronavirus disruption, online health consultations, involvement of healthcare providers, and remote monitoring of patients are becoming increasingly accepted.

New approaches to delivering healthcare services

Improvement in healthcare can be achieved irrespective of geographies. New healthcare approaches are paving way with modern infrastructure, service requirements and analytics.

1. Enhanced pharmacy experience: Pharmacies are expanding their touchpoints to provide medicines and other healthcare services. Grocery stores are also being used to sell drugs along with other products to retain customers and give them single time purchase experiences. Many leading brands are exploring options to set up healthcare retailing and experimenting delivery of medicines using drones.

2. Increase in contactless experience: Coronavirus apprehension is more prevalent in clinics and hospitals. Technology-enabled workflows are used to improve patients’ registration before visiting a clinic. Facial recognition programs are used to remove the need of touch, requiring registration stands in hospital lobbies. Routine tests are also getting virtual, and many diagnostic procedures via remote-controlled devices are increasing. With the rise of bandwidth, modern mobile apps, and desire to isolate themselves from society throughout the pandemic, both clinicians and consumers are preferring virtual calls. Virtual video care is been initiated in many locations around the world to reduce long-distance travel for patients.

3. Big Data in healthcare: Big data uses the collection of all data points on COVID-19 from around the world. These data are used by mathematical models to identify geographic locations, establish mortality prediction models, estimate testing and test delivery requirements, and guide policymakers, healthcare providers, and other key players in decision making. It is essential to note that while big data gives us the perspective that we wouldn’t have otherwise, all variables required to make a specific decision are not automatically considered.

4. AI and data analytics: Health industry’s defensive line against COVID-19 was primarily helped by AI and data analytics. Machine Learning and data analysis have a significant role in understanding the spread of the disease and the efficacy of the different responses to the infection. Studies have utilized these methods for monitoring the capacity of hospitals to classify high-risk patients, and many agree that AI could be used to plan for similar situations in the future.

Conclusion

A seamless product design is to gain an intuitive understanding of the trajectory of patients in the modern post-pandemic period and to identify high-impact touchpoints for digital interaction. Every patient population varies in their digital communication preferences, whether by socioeconomic status or other demographic factors. Each health system must develop a digital environment suitable for its patients, while continuing to address the needs of caregivers who provide and maintain the environment

Disruptive Technology for Healthcare

Disruptive-Technology-for-Healthcare

With immense technological advancement, global healthcare industry will transform tremendously and move towards digital. Factors like increasing population, declining healthcare budgets, and rise of chronic diseases add pressure on healthcare providers and governments to shift to innovative technologies. 

Consequently, the global healthcare industry is ideal for driving new technological advancements like Internet of Things (IoT). Smart devices such as smartphones, smartwatches, and other new emerging technologies will act as driving forces of this revolution.   

Let’s discuss some of the technologies that could transform the healthcare industry in the future. 

AI and Machine Learning

Machine Learning is a form of AI that offers learning capabilities to computers without being explicitly programmed. It means that computers can teach themselves to modify according to the need when exposed to new data. With AI, there will be a considerable amount of data to explore. Several tech conglomerates like Google, IBM, etc. have already started exploring these technologies’ potential applications in healthcare.  

There are truly exciting possibilities for the application of AI/ML for such digital surgery robots. 

  • A software-centric collaboration of robots with the aid of massive distributed processing 
  • Data-driven insights and guidance based on surgery histories (performed by both machines and humans) and their outcomes (favorable or not) 
  • AI-generated virtual reality space for real-time direction and guidance 
  • Possibility of telemedicine and remote surgery for relatively simple procedures 

Digital Therapy

These are healthcare interventions delivered to patients through smart devices like smartphones or laptops. They combine medical practice and therapy in a digital form. Computerized Cognitive Behavioral Therapy (CBT) is a new group of automated digital therapies that aims to provide CBT at scale with better engagement.  

“Digital Therapeutics: Combining Technology and Evidence-based Medicine to Transform Personalized Patient Care” 

Digital therapies are disease-specific treatment tools. Often, they are a substitute treatment (or the only treatment) with sensory stimuli; in other situations, they support or enhance conventional medicine incorporating electronic usage with tools or medications.

For example, some devices complement conventional treatment by helping patients manage and control their conditions, providing indicators such as remembering when and how much medication to take.

Apps and Smartphones

The full potential of smartphones is yet to be perceived by the healthcare sector. Companies are making efforts to curate quality apps, such as the NHS (National Health Service) app library. With powerful processing capabilities, smartphones can serve as the hub for new diagnoses and treatments.

Although the use of mHealth devices and applications is already common in clinical trials, pharmaceutical companies are now concentrating on connected drug delivery systems that will automatically identify and monitor medication usage by patients to enhance adherence. 

Health apps and smartphones help to: track personal health data, real-time communication with your doctor or another healthcare provider, and improve the quality of life for doctors and their patients. 

Portable Diagnostics

With devices like portable X-ray machines, we can bring cutting-edge diagnostics at our doorstep. Doctors can provide better quality of care by more profound and meaningful engagements with patients. It also leads to the continuous capturing of crucial health-related information, which will eventually reduce the overall healthcare costs. Assistive devices like smart wheelchairs are used by patients with permanent disabilities, which help them perform specific tasks and gather essential health information. This information can later be used for modifying treatment procedures.  

Online Communities

Online communities and famous healthcare networks like MedHelp bring medical experts and patients together to share health advice and tips. They also serve as a platform for tracking health data, which helps people better manage their conditions.  

Advanced technologies like these offer new opportunities to the healthcare system in improving correctness and usefulness of medical information. It also provides new ways to prevent, detect, and treat diseases at early stages rather than reach the terminal phase.  

HealthTech

Implantable Drug Delivery Systems  

It is estimated that nearly one-third of all medication prescribed to patients with long-term health conditions are not adequately taken as recommended by the physicians. Emerging technologies could change this by enabling healthcare professionals with continuous monitoring capabilities.  

In the future there could be sensors that are tiny and can be swallowed along with drugs. As soon as the pill dissolves in the patient’s stomach, the sensor will get activated and transmit data to a smartphone app. Patients and doctors can see how well they are adhering to the prescription, though it raises questions about the patient’s privacy.  

Blockchain

Blockchains are decentralized databases that keep record of how data is generated and changed over time. The main feature of Blockchains is that it can be trusted, as the records are authentic without a central authority guaranteeing accuracy and security.  

Though Electronic Health Records are commonly used, they are usually centralized. Some analysts state that Blockchain would bring several benefits to patients and doctors as compared to other records.  

Genome Sequencing

Breakthroughs in genome sequencing and its associated field will help us better understand the diseases. Genome sequencing gives a genetic profile of a patient’s illness by which doctors can predict their treatment.  

Globally, several big projects are underway to understand the association between genes and health conditions. In the UK, the government is funding 100,000 Genomes projects. In the US, one company has promised to build a database featuring 1 million genomes by 2020.  

Conclusion

Maintaining patient care in today’s hyper-connected environment depends almost entirely on maintaining and leveraging their network and services. Network administrators need to be vigilant and disciplined – not just for performance, but to prevent security disruptions. 

CIOs and CCIOs (chief clinical information officers) in healthcare organizations face the urgent need to keep pace with technology. They introduce next-generation technologies in an attempt to improve overall efficiency, speed and safety. 

Healthcare is evolving, and modern technologies will transform human life for the better again, just as antibiotics and anesthesia have changed for decades. For hospitals and suppliers working together to protect data, the future seems bright. 

Extracting Acronyms through Natural Language Processing

Alex Thompson Data and AI October 21, 2020
Extracting-Acronyms-through-Natural-Language-Processing

Introduction  

An acronym is a pronounceable word created from the first letter of each word in a phrase or title. An acronym is a kind of abbreviation consisting of a first letter or initial letters in a word. It’s also called short descriptors of phrase.  

Interesting Fact: Acronym was introduced as a modern linguistic element of English during the 1950s. Because acronym is called a term, its meaning is called expansion.  

Usage & Challenges 

An acronym is primarily used in language processing, web search, ontology mapping, question answering, text messaging, and social media sharing. Acronyms evolve each day dynamically, and finding their definition/expansion becomes a daunting task due to its diverse characteristics. Several researchers experimented with plain text and network expansion pairs for mining acronyms over the past two decades. Manually edited online archives have pairs of acronyms, but regularly reviewing all possible meanings is intimidating.  

Solution 

To handle this issue, TVS Next has built a specialized product to extract acronyms from a document in a few seconds. This product is built on Python for Natural Language Processing.  

Below are some pointers that describe how our research works that help us solve the problem mentioned above.   

Heuristics Approach 

NLP (Natural Language Processing) and pattern-based methods include heuristics. 

  • The NLP-based approach uses a fuzzy-matching Statistical Model based on the principles of Levenshtein’s Distance algorithm.  
  • The pattern-based approach uses custom rules that work with data from multiple domains, combined with Statistical Modelling to extract the Acronyms and their Expansions. These methods are written after considering features in the text as characteristics of acronyms – ambiguity, nesting, uppercase letters, length, and para-linguistic markers.  

An Acronym Finding Program (AFP) is a simple, free-text expansion recognition method. This program applies an inexact matching algorithm for mining AE pairs. A tool known as Three Letter Acronym (TLA) uses para-linguistic markers such as parenthesis, commas, and periods to derive acronym meaning from technical and government documents.  

Developing the Product  

A Statistical model has created to provide the user with a Solution that gives ease of access to acronyms that appear throughout the document. The designed solution can be integrated into various tools and technologies that deal with text-based information. The solution proves to be useful while combining it with tools that parse PDF documents. It deals with – tables, free-flowing text.  

A document consists of multiple tables that are very similar in structure; hence our solution uses a Table Classification method to differentiate the acronym table from the rest. Various types of Statistical Methods were incorporated to quantify features/patterns that help define what an acronym will look like. This solution was used to classify an acronym table from the rest and then extract acronyms from the table.   

For free-flowing text, a similar technique has been used where the patterns/features of an acronym are incorporated to differentiate it from the rest of the free-flowing text. There are words extracted that can turn out to be acronyms. These words appear along with their expansion in the text. After extracting suspected acronyms, we quantify the words that consist of acronyms using statistical models and compare them to their expansions.  

By enforcing the following statistical models, 80% of acronyms are obtained that are present in a document. It is essential to accommodate variations in how text is written. Simple human punctuation errors can affect the entire acronym, not falling under rules of how acronyms are generally written. A dynamic method where custom rules that works with data from multiple domains are combined along with specific Statistical Models has been implemented that will uncommonly parse texts.  

On executing this dynamic method and testing various documents, we could conclude that the Statistical Model-based acronym extraction method has been performing with over 95% accuracy, even surpassing open source solutions provided by Spacy called Blackstone available in the market at the moment. Blackstone works on the techniques mentioned in a research paper written by Ariel S Schwartz et al. [2]., Multiple comparisons were made, between Blackstone and the Statistical-method based Acronym Extraction.

Result  

The Statistical Model-based acronym extraction method scanned an entire document of 100+ pages in milliseconds and displayed 98% accuracy. The average time taken to scan a document is a few seconds, and the accuracy of this product has been achieved between 94-98%. The product was tested on documents belonging to various domains, and it still yielded similar results. The product is developed on an experimental basis, and we are set to improve its efficiency and performance each day. There is plenty of room for improvement with subject to market changes. The product experiments with a set of Statistical models and custom rules, and the team is working on dynamic changes using AI that scans documents based on results. This product proves to be useful for lengthy and complicated engineering and medical documents. This product is one of its kind, and we are proud of our development.

At TVS Next, we re-imagine, design, and develop software to enable our clients to build a better world.  


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